Channel prediction using deep recurrent neural network with EVT-Based adaptive quantile loss function

Abstract

Ultra-reliable low latency communication (URLLC) systems are pivotal for applications demanding high reliability and low latency, such as autonomous vehicles. In such contexts, channel prediction becomes essential to maintaining communication quality, as it allows the system to anticipate and mitigate the effects of fast-fading channels, thereby reducing the risk of packet loss and latency spikes. This letter presents a novel framework that integrates neural networks with extreme value theory (EVT) to enhance channel prediction, focusing on predicting extreme channel events that challenge URLLC performance. We propose an EVT-based adaptive quantile loss function that integrates EVT into the loss function of the deep recurrent neural networks (DRNNs) with gated recurrent units (GRUs) to predict extreme channel conditions efficiently. The numerical results indicate that the proposed GRU model, utilizing the EVT-based adaptive quantile loss function, significantly outperforms the traditional GRU. It predicts a tail portion of 7.26%, which closely aligns with the empirical 7.49%, while the traditional GRU model only predicts 2.4%. This demonstrates the superior capability of the proposed model in capturing tail values that are critical for URLLC systems.